Topological Data Analysis and Wavelet- Unsupervised Machine Learning Approaches to Identifying the Flooding and Non-Flooding Zones by Md Raqibul Hasan, Md. Jamal Hossain, Md. Waliullah, Abdul Hannan, Md. Mijanoor Rahman

“…The intertwined weather patterns known as the atmospheric river (AR) of Bangladesh make use of topological data analysis (TDA) in connection with wavelet decomposition and unsupervised machine learning (k-means clustering) methods to pave the way for enhanced flood detection. …”
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Leveraging Artificial Intelligence for Smart Healthcare Management: Predicting and Reducing Patient Waiting Times with Machine Learning by Kristijan CINCAR, Todor IVAŞCU

“… The paper focuses on a machine-learning-based methodology for predictive modelling and simulation enhancement of hospital resource management. …”
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Machine Learning-Based Software for Predicting <i>Pseudomonas</i> spp. Growth Dynamics in Culture Media by Fatih Tarlak

“…Machine learning models provided superior accuracy over traditional approaches, with R²_adj values from 0.834 to 0.959 and RMSE values between 0.005 and 0.010, showcasing their ability to handle complex growth patterns more effectively. …”
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Improving ICESat-2 photon classification and tree height estimation using Moran's I and machine learning by Mei-Kuei Lu, Sorin Popescu, Lonesome Malambo

“…Random Forest models were developed and compared, with one model incorporating Moran's I to capture spatial patterns. The study covered 12 diverse ecoregions across the United States, including conifer forests, broadleaf forests, and savannas. …”
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Modeling Equatorial to Mid‐Latitudinal Global Night Time Ionospheric Plasma Irregularities Using Machine Learning by Ephrem Beshir Seba, Giovanni Lapenta

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Machine Learning and SHAP-Based Analysis of Deforestation and Forest Degradation Dynamics Along the Iraq–Turkey Border by Milat Hasan Abdullah, Yaseen T. Mustafa

“…This study explores the spatiotemporal patterns and drivers of deforestation and forest degradation along the politically sensitive Iraq–Turkey border within the Duhok Governorate between 2015 and 2024. …”
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Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…The joint interpretation of these statistics aids in comprehending model limitations and facilitates discussions on the environmental mechanisms shaping observed patterns. We propose two analytical workflows that not only enable the exploration and enhancement of model accuracy but also facilitate the investigation of potential cause-and-effect relationships inherent in the  data. …”
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A Spatial Long-Term Load Forecast Using a Multiple Delineated Machine Learning Approach by Terence Kibula Lukong, Derick Nganyu Tanyu, Yannick Nkongtchou, Thomas Tamo Tatietse, Detlef Schulz

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Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…The joint interpretation of these statistics aids in comprehending model limitations and facilitates discussions on the environmental mechanisms shaping observed patterns. We propose two analytical workflows that not only enable the exploration and enhancement of model accuracy but also facilitate the investigation of potential cause-and-effect relationships inherent in the  data. …”
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Machine learning supported ground beef freshness monitoring based on near‐infrared and paper chromogenic array by Yihang Feng, Yi Wang, Burcu Beykal, Zhenlei Xiao, Yangchao Luo

“…Changes in ground beef volatile organic compounds during storage were captured in the shifts of PCA color patterns. Nippy, an open‐source Python module, was used for automated NIR spectra preprocessing. …”
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Investigating bearing and gear vibrations with a Micro-Electro-Mechanical Systems (MEMS) and machine learning approach by Gagandeep Sharma, Tejbir Kaur, Sanjay Kumar Mangal, Amit Kohli

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A multidimensional machine learning framework for LST reconstruction and climate variable analysis in forest fire occurrence by Hatef Dastour, Quazi K. Hassan

“…The findings revealed complex interactions, with high LST, reduced precipitation, and humidity associated with increased forest fire activity and subsequent changes in vegetation patterns, particularly in the Central Mixedwood, Dry Mixedwood, and Montane subregions. …”
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State-of-the-Art Fault Detection and Diagnosis in Power Transformers: A Review of Machine Learning and Hybrid Methods by Lebo Dina Mashifane, Bongumsa Mendu, Bessie Baakanyang Monchusi

“…It identifies key research patterns, trends, and themes, while also highlighting gaps and offering suggestions for future research to improve diagnostics and monitoring.…”
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Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach by Sumaira Tabassum, M. Jawad Khan, Javaid Iqbal, Asim Waris, M. Adeel Ijaz

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The construction of HMME-PDT efficacy prediction model for port-wine stain based on machine learning algorithms by Hongxia Yan, Yixin Tan, Fan Qiao, Zhuotong Zeng, Yaqian Shi, Xueqin Zhang, Lu Li, Ting Zeng, Yi Zhan, Ruixuan You, Xinglan He, Rong Xiao, Xiangning Qiu

“…RFE identified the top predictive factors: dermoscopy vascular pattern, immediate fluorescence intensity (IFI) after HMME-PDT, the facial port-wine stain area and severity index score, and age. …”
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Enhancing prediction of crop yield and soil health assessment for sustainable agriculture using machine learning approach by Kapil Netaji Vhatkar, Shweta Ashish Koparde, Sonali Kothari, Jayesh Sarwade, Kishor Sakur

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A two-step machine learning approach for predictive maintenance and anomaly detection in environmental sensor systems by Saiprasad Potharaju, Ravi Kumar Tirandasu, Swapnali N. Tambe, Devyani Bhamare Jadhav, Dudla Anil Kumar, Shanmuk Srinivas Amiripalli

“…Using Environmental Sensor Telemetry Data, this study introduces a novel methodology that combines unsupervised and supervised machine learning approaches to detect anomalies and predict sensor failures. …”
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Multivariate Machine Learning Model Based on YOLOv8 for Traffic Flow Prediction in Intelligent Transportation Systems by Fukui Wu, Hanzhong Tan, Linfeng Zhang, Shuangbing Wen, Tao Hu

“…Subsequently, five machine learning algorithms and three deep learning algorithms are employed to predict traffic flow. …”
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Predicting salinity levels in the Mekong delta (Viet Nam): analysis of machine learning and deep learning models by Phong Nguyen Duc, Thang Tang Duc, Giap Pham Van, Hoat Nguyen Van, Tuan Tran Minh

“…The LSTM structure has proven to be effective at capturing long-term temporal dependencies, such as seasonal discharge patterns, while XGB successfully models non-linear interactions between stations with the greatest success, particularly discharge-tidal level interactions. …”
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Antifungal Susceptibility Testing in HIV/AIDS Patients: a Comparison Between Automated Machine and Manual Method by Erni J Nelwan, Evi Indrasanti, Robert Sinto, Farida Nurchaida, Rustadi Sosrosumihardjo

“…Resistant patterns for C. glabrata to fluconazole, voriconazole and amphotericin B were 52.4%, 23.8%, 23.8% vs. 9.5%, 9.5%, 4.8% respectively between manual diffusion disc methods and Vitek2 machine. …”
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